Structural representation and decision-making in an interconnected world
Social networks shape our beliefs and choices by constraining what information we receive and from whom. Yet the mechanism by which the human brain interacts with networked environments remains unclear. Two computational challenges stand out when we try to learn from interconnected peers. First, information flowing along network connections is typically interdependent and varies in its informativeness, so how does the brain effectively integrate network-derived information? Second, individuals can hardly take into account the topological structure of the entire network when interacting with it. So which social connections are considered and which are ignored, how will the streamlined network representation affect our perception and navigation of the social world? In this talk, I will present a series of recent work that uses computational modeling, fMRI, and graph neural networks to investigate social network-related learning and representation. Our finding unifies a variety of seemingly disparate biases in social perception and decision-making, shedding light on the cognitive roots of some important societal conundrums, such as biased social sensing and misinformation propagation.
Date:
14 May 2024, 13:00 (Tuesday, 4th week, Trinity 2024)
Venue:
New Radcliffe House, Walton Street OX2 6NW
Venue Details:
Seminar room, second floor.
Speaker:
Dr Lusha Zhu (Peking University)
Organising department:
Department of Experimental Psychology
Organisers:
Dr Nima Khalighinejad (University of Oxford),
Dr Lauren Burgeno (University of Oxford)
Organiser contact email address:
qingtian.mi@psy.ox.ac.uk
Host:
Qingtian Mi (University of Oxford)
Part of:
Department of Experimental Psychology - Cognitive & Behavioural Neuroscience Seminar series (BEACON)
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Anne-Marie Honeyman-Tafa
